Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using categories that you feel may influence fuel efficiency. Answer the following questions.
Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot?
Answer: HorsePower and Weight - The categories will indictate that the higher the weight and horsepower, the more gas the car will use.
Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Weight is y- axis and horsepower is the x-axis.
Do you believe there is a relationship between the two categories? Why or why not?
Answer: Yes. The higher the horsepower and the heavier the car, the more gas the car will use.
If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Answer: Positive slope. As the horsepower and the weight increases, the slope goes upward indicating the use of more gas.
Regression Questions
Create the linear regession equation in Excel. Include both the equation and the r2 value on the graph. Answer the following questions.
What is your regression equation? Explain what the equation means in relation to the categories.
Answer: y=8.321x + 1876
What is your r2 value? Is this a strong correlation? Why or Why not?
Answer: 0.545
Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not?
Answer:
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
How would you determine which equation had the best relationship?
Answer:
Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Answer:
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.
Back to Activity1Begin by examining the data set. Recognize how the data is recorded and how you may be able to use the given data to explore potential relationships between categories.
Scatterplot Questions
1. Create a scatterplot using categories that you feel may influence fuel efficiency. Answer the following questions.
Answer: HorsePower and Weight - The categories will indictate that the higher the weight and horsepower, the more gas the car will use.Identify the two categories you chose and why you thought there might be a relationship between the two BEFORE creating the scatterplot?
Answer: Weight is y- axis and horsepower is the x-axis.Create the scatterplot. Which category is your x-axis and which is your y-axis? Why did you create your scatterplot in that order?
Answer: Yes. The higher the horsepower and the heavier the car, the more gas the car will use.Do you believe there is a relationship between the two categories? Why or why not?
Answer: Positive slope. As the horsepower and the weight increases, the slope goes upward indicating the use of more gas.If there appears to be a relationship, does it have a positive or negative slope? What does this mean about the relationship between the two categories?
Regression Questions
Create the linear regession equation in Excel. Include both the equation and the r2 value on the graph. Answer the following questions.
Answer: y=8.321x + 1876What is your regression equation? Explain what the equation means in relation to the categories.
Answer: 0.545What is your r2 value? Is this a strong correlation? Why or Why not?
Answer:Based on all the information you have, can you make any conclusions about your two categories? If so, what conclusions can you make? If not, why not?
Analysis
Right click on the regression equation and select "Format Trendline". Explore the different variations of regression equations.
Answer:How would you determine which equation had the best relationship?
Answer:Was the "Linear" option the optimal option? If so, why? If not, what was the better equation and why?
Attach your Scatter Plots and Regression Information. Make sure your X and Y axis are correctly labeled. You may use Screen Shots to do so.